Overview

Dataset statistics

Number of variables13
Number of observations710
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.2 KiB
Average record size in memory104.2 B

Variable types

Numeric12
DateTime1

Alerts

gpu_memory_usage has constant value "3362783232.0" Constant
unpaid has constant value "127800788" Constant
df_index is highly correlated with relative_hourHigh correlation
gpu_load is highly correlated with reported_hashrateHigh correlation
reported_hashrate is highly correlated with gpu_loadHigh correlation
relative_hour is highly correlated with df_indexHigh correlation
df_index is uniformly distributed Uniform
relative_hour is uniformly distributed Uniform
df_index has unique values Unique
ts has unique values Unique
relative_hour has unique values Unique
hashrate has 55 (7.7%) zeros Zeros

Reproduction

Analysis started2021-12-01 03:32:51.115431
Analysis finished2021-12-01 03:33:09.996834
Duration18.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct710
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533.5
Minimum179
Maximum888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:10.069532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum179
5-th percentile214.45
Q1356.25
median533.5
Q3710.75
95-th percentile852.55
Maximum888
Range709
Interquartile range (IQR)354.5

Descriptive statistics

Standard deviation205.1036323
Coefficient of variation (CV)0.3844491703
Kurtosis-1.2
Mean533.5
Median Absolute Deviation (MAD)177.5
Skewness0
Sum378785
Variance42067.5
MonotonicityStrictly increasing
2021-11-30T22:33:10.214544image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1791
 
0.1%
6231
 
0.1%
6471
 
0.1%
6481
 
0.1%
6491
 
0.1%
6501
 
0.1%
6511
 
0.1%
6521
 
0.1%
6531
 
0.1%
6541
 
0.1%
Other values (700)700
98.6%
ValueCountFrequency (%)
1791
0.1%
1801
0.1%
1811
0.1%
1821
0.1%
1831
0.1%
ValueCountFrequency (%)
8881
0.1%
8871
0.1%
8861
0.1%
8851
0.1%
8841
0.1%

ts
Date

UNIQUE

Distinct710
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Minimum2021-11-10 09:01:12-05:00
Maximum2021-11-10 11:03:40-05:00
2021-11-30T22:33:10.372786image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:10.544689image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cpu_load
Real number (ℝ≥0)

Distinct12
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2494366197
Minimum0.1
Maximum4.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:10.713872image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.2
Q30.3
95-th percentile0.6
Maximum4.2
Range4.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.2036160227
Coefficient of variation (CV)0.8163036483
Kurtosis201.828569
Mean0.2494366197
Median Absolute Deviation (MAD)0
Skewness11.23681802
Sum177.1
Variance0.04145948469
MonotonicityNot monotonic
2021-11-30T22:33:10.838874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.2427
60.1%
0.3131
 
18.5%
0.187
 
12.3%
0.626
 
3.7%
0.711
 
1.5%
0.510
 
1.4%
0.49
 
1.3%
0.83
 
0.4%
0.92
 
0.3%
12
 
0.3%
Other values (2)2
 
0.3%
ValueCountFrequency (%)
0.187
 
12.3%
0.2427
60.1%
0.3131
 
18.5%
0.49
 
1.3%
0.510
 
1.4%
ValueCountFrequency (%)
4.21
 
0.1%
1.41
 
0.1%
12
0.3%
0.92
0.3%
0.83
0.4%

cpu_freq
Real number (ℝ≥0)

Distinct694
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean927.4809859
Minimum806.39
Maximum3593.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:10.959393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum806.39
5-th percentile819.736
Q1845.2125
median875.05
Q3920.03
95-th percentile1129.982
Maximum3593.58
Range2787.19
Interquartile range (IQR)74.8175

Descriptive statistics

Standard deviation252.3489461
Coefficient of variation (CV)0.2720799132
Kurtosis67.79233712
Mean927.4809859
Median Absolute Deviation (MAD)34.515
Skewness7.574449871
Sum658511.5
Variance63679.99061
MonotonicityNot monotonic
2021-11-30T22:33:11.081837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
845.884
 
0.6%
851.323
 
0.4%
932.372
 
0.3%
856.412
 
0.3%
841.852
 
0.3%
887.892
 
0.3%
856.022
 
0.3%
821.682
 
0.3%
834.052
 
0.3%
901.082
 
0.3%
Other values (684)687
96.8%
ValueCountFrequency (%)
806.391
0.1%
807.551
0.1%
808.231
0.1%
808.891
0.1%
808.941
0.1%
ValueCountFrequency (%)
3593.581
0.1%
3592.61
0.1%
3511.411
0.1%
3162.181
0.1%
2932.661
0.1%

memory_usage
Real number (ℝ≥0)

Distinct616
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1462859528
Minimum1451540480
Maximum1503039488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:11.212934image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1451540480
5-th percentile1458314445
Q11460296704
median1462634496
Q31465463808
95-th percentile1466769408
Maximum1503039488
Range51499008
Interquartile range (IQR)5167104

Descriptive statistics

Standard deviation4591683.8
Coefficient of variation (CV)0.00313884123
Kurtosis35.6236685
Mean1462859528
Median Absolute Deviation (MAD)2615296
Skewness4.089729664
Sum1.038630265 × 1012
Variance2.108356012 × 1013
MonotonicityNot monotonic
2021-11-30T22:33:11.356020image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14596792323
 
0.4%
14646599683
 
0.4%
14599331843
 
0.4%
14657290243
 
0.4%
14661140483
 
0.4%
14606745602
 
0.3%
14652907522
 
0.3%
14588477442
 
0.3%
14593597442
 
0.3%
14664294402
 
0.3%
Other values (606)685
96.5%
ValueCountFrequency (%)
14515404801
0.1%
14519582721
0.1%
14520811521
0.1%
14521139201
0.1%
14523351041
0.1%
ValueCountFrequency (%)
15030394881
0.1%
15029698561
0.1%
15020482561
0.1%
15011389441
0.1%
15009505281
0.1%

cpu_temp
Real number (ℝ≥0)

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.87042254
Minimum29
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:11.471443image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile33
Q135
median35
Q335
95-th percentile36
Maximum37
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.153934139
Coefficient of variation (CV)0.03309206068
Kurtosis5.161592544
Mean34.87042254
Median Absolute Deviation (MAD)0
Skewness-1.739297127
Sum24758
Variance1.331563996
MonotonicityNot monotonic
2021-11-30T22:33:11.559960image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
35367
51.7%
36155
21.8%
34114
 
16.1%
3325
 
3.5%
3719
 
2.7%
3110
 
1.4%
3210
 
1.4%
309
 
1.3%
291
 
0.1%
ValueCountFrequency (%)
291
 
0.1%
309
 
1.3%
3110
 
1.4%
3210
 
1.4%
3325
3.5%
ValueCountFrequency (%)
3719
 
2.7%
36155
21.8%
35367
51.7%
34114
 
16.1%
3325
 
3.5%

gpu_memory_usage
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3362783232
Minimum3362783232
Maximum3362783232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:11.655883image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum3362783232
5-th percentile3362783232
Q13362783232
median3362783232
Q33362783232
95-th percentile3362783232
Maximum3362783232
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean3362783232
Median Absolute Deviation (MAD)0
Skewness0
Sum2.387576095 × 1012
Variance0
MonotonicityIncreasing
2021-11-30T22:33:11.749123image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
3362783232710
100.0%
ValueCountFrequency (%)
3362783232710
100.0%
ValueCountFrequency (%)
3362783232710
100.0%

gpu_load
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.99577465
Minimum99
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:11.964651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06491096151
Coefficient of variation (CV)0.0006491370435
Kurtosis233.3192051
Mean99.99577465
Median Absolute Deviation (MAD)0
Skewness-15.31868053
Sum70997
Variance0.004213432925
MonotonicityNot monotonic
2021-11-30T22:33:12.048799image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
100707
99.6%
993
 
0.4%
ValueCountFrequency (%)
993
 
0.4%
100707
99.6%
ValueCountFrequency (%)
100707
99.6%
993
 
0.4%

gpu_temp
Real number (ℝ≥0)

Distinct26
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.05070423
Minimum36
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:12.153796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile75
Q176
median77
Q377
95-th percentile78
Maximum78
Range42
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.706087442
Coefficient of variation (CV)0.0487317965
Kurtosis61.79778057
Mean76.05070423
Median Absolute Deviation (MAD)0
Skewness-7.37776545
Sum53996
Variance13.73508413
MonotonicityNot monotonic
2021-11-30T22:33:12.343935image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
77361
50.8%
76230
32.4%
7856
 
7.9%
7528
 
3.9%
746
 
0.8%
734
 
0.6%
724
 
0.6%
702
 
0.3%
692
 
0.3%
391
 
0.1%
Other values (16)16
 
2.3%
ValueCountFrequency (%)
361
0.1%
391
0.1%
421
0.1%
451
0.1%
471
0.1%
ValueCountFrequency (%)
7856
 
7.9%
77361
50.8%
76230
32.4%
7528
 
3.9%
746
 
0.8%

hashrate
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057034.561
Minimum0
Maximum1676815.12
Zeros55
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:12.521312image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1598862.54
median1077952.57
Q31676815.12
95-th percentile1676815.12
Maximum1676815.12
Range1676815.12
Interquartile range (IQR)1077952.58

Descriptive statistics

Standard deviation615940.3332
Coefficient of variation (CV)0.5827059549
Kurtosis-1.329244606
Mean1057034.561
Median Absolute Deviation (MAD)598862.55
Skewness-0.4569364487
Sum750494538.2
Variance3.793824941 × 1011
MonotonicityNot monotonic
2021-11-30T22:33:12.668113image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1676815.12228
32.1%
119772.5160
 
8.5%
359317.5360
 
8.5%
838407.5660
 
8.5%
1437270.160
 
8.5%
1557042.6160
 
8.5%
1077952.5760
 
8.5%
055
 
7.7%
598862.5454
 
7.6%
958180.0713
 
1.8%
ValueCountFrequency (%)
055
7.7%
119772.5160
8.5%
359317.5360
8.5%
598862.5454
7.6%
838407.5660
8.5%
ValueCountFrequency (%)
1676815.12228
32.1%
1557042.6160
 
8.5%
1437270.160
 
8.5%
1077952.5760
 
8.5%
958180.0713
 
1.8%

unpaid
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127800788
Minimum127800788
Maximum127800788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:12.766224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum127800788
5-th percentile127800788
Q1127800788
median127800788
Q3127800788
95-th percentile127800788
Maximum127800788
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean127800788
Median Absolute Deviation (MAD)0
Skewness0
Sum9.073855948 × 1010
Variance0
MonotonicityIncreasing
2021-11-30T22:33:12.847346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
127800788710
100.0%
ValueCountFrequency (%)
127800788710
100.0%
ValueCountFrequency (%)
127800788710
100.0%

reported_hashrate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1249947.183
Minimum1237500
Maximum1250000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:12.928606image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1237500
5-th percentile1250000
Q11250000
median1250000
Q31250000
95-th percentile1250000
Maximum1250000
Range12500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation811.3870189
Coefficient of variation (CV)0.0006491370435
Kurtosis233.3192051
Mean1249947.183
Median Absolute Deviation (MAD)0
Skewness-15.31868053
Sum887462500
Variance658348.8945
MonotonicityNot monotonic
2021-11-30T22:33:13.017663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1250000707
99.6%
12375003
 
0.4%
ValueCountFrequency (%)
12375003
 
0.4%
1250000707
99.6%
ValueCountFrequency (%)
1250000707
99.6%
12375003
 
0.4%

relative_hour
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct710
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.534937402
Minimum0.5144444444
Maximum2.555555556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2021-11-30T22:33:13.135184image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.5144444444
5-th percentile0.616375
Q11.025138889
median1.534722222
Q32.045069444
95-th percentile2.453347222
Maximum2.555555556
Range2.041111111
Interquartile range (IQR)1.019930556

Descriptive statistics

Standard deviation0.590297892
Coefficient of variation (CV)0.3845745704
Kurtosis-1.199101986
Mean1.534937402
Median Absolute Deviation (MAD)0.5106944444
Skewness-6.640972344 × 10-5
Sum1089.805556
Variance0.3484516013
MonotonicityStrictly increasing
2021-11-30T22:33:13.281801image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51444444441
 
0.1%
1.79251
 
0.1%
1.8613888891
 
0.1%
1.8644444441
 
0.1%
1.8672222221
 
0.1%
1.8702777781
 
0.1%
1.8730555561
 
0.1%
1.8758333331
 
0.1%
1.8788888891
 
0.1%
1.8816666671
 
0.1%
Other values (700)700
98.6%
ValueCountFrequency (%)
0.51444444441
0.1%
0.51722222221
0.1%
0.52027777781
0.1%
0.52305555561
0.1%
0.52583333331
0.1%
ValueCountFrequency (%)
2.5555555561
0.1%
2.55251
0.1%
2.5497222221
0.1%
2.5469444441
0.1%
2.5441666671
0.1%

Interactions

2021-11-30T22:33:08.039266image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:51.388756image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:52.767025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:54.304506image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:55.768713image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:57.391155image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:58.932864image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:00.407147image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:01.902411image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:03.411262image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:04.968617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:06.465080image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:33:08.158264image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:51.502989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:52.873953image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:54.415870image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:55.894877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-30T22:32:57.507879image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2021-11-30T22:33:13.417972image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-30T22:33:13.595344image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-30T22:33:13.775635image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-30T22:33:13.949634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-30T22:33:09.662549image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-30T22:33:09.918884image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
01792021-11-10 09:01:12-05:004.2966.93150113894430.03.362783e+09100.036.00.01278007881250000.00.514444
11802021-11-10 09:01:22-05:000.2897.72150095052829.03.362783e+09100.039.00.01278007881250000.00.517222
21812021-11-10 09:01:33-05:000.3921.23150204825630.03.362783e+09100.042.00.01278007881250000.00.520278
31822021-11-10 09:01:43-05:000.1838.16150303948830.03.362783e+09100.045.00.01278007881250000.00.523056
41832021-11-10 09:01:53-05:000.2847.66150296985630.03.362783e+09100.047.00.01278007881250000.00.525833
51842021-11-10 09:02:04-05:000.9990.80145307648030.03.362783e+09100.051.00.01278007881250000.00.528889
61852021-11-10 09:02:14-05:000.21117.24145233510431.03.362783e+09100.053.00.01278007881250000.00.531667
71862021-11-10 09:02:24-05:000.3888.93145211392030.03.362783e+09100.055.00.01278007881250000.00.534444
81872021-11-10 09:02:35-05:000.2862.71145242521630.03.362783e+09100.058.00.01278007881250000.00.537500
91882021-11-10 09:02:45-05:000.2874.39145244160031.03.362783e+09100.060.00.01278007881250000.00.540278

Last rows

df_indextscpu_loadcpu_freqmemory_usagecpu_tempgpu_memory_usagegpu_loadgpu_temphashrateunpaidreported_hashraterelative_hour
7008792021-11-10 11:02:07-05:000.1834.56146046976036.03.362783e+09100.077.0958180.071278007881250000.02.529722
7018802021-11-10 11:02:17-05:000.4938.36145863065636.03.362783e+09100.078.0958180.071278007881250000.02.532500
7028812021-11-10 11:02:27-05:000.2878.33145771724835.03.362783e+09100.077.0958180.071278007881250000.02.535278
7038822021-11-10 11:02:38-05:000.2838.04145828249636.03.362783e+09100.078.0958180.071278007881250000.02.538333
7048832021-11-10 11:02:48-05:000.2888.92145833984035.03.362783e+09100.077.0958180.071278007881250000.02.541111
7058842021-11-10 11:02:59-05:000.3814.87146037964837.03.362783e+09100.078.0958180.071278007881250000.02.544167
7068852021-11-10 11:03:09-05:000.2841.65145924096035.03.362783e+09100.078.0958180.071278007881250000.02.546944
7078862021-11-10 11:03:19-05:000.3948.32145863884836.03.362783e+09100.078.0958180.071278007881250000.02.549722
7088872021-11-10 11:03:29-05:000.41696.38145888870436.03.362783e+09100.078.0958180.071278007881250000.02.552500
7098882021-11-10 11:03:40-05:000.61127.43146087526436.03.362783e+09100.078.0958180.071278007881250000.02.555556